Big Data Analysis for Effective Management of Power Distribution Network

Abstract

To find a way to manage power distribution networks efficiently, we researched the use of big data analysis and established a model with mathematical functions to assess the benefit, risk, and economy of the power supply in a power distribution network. The necessary data were collected from the sensors in the network and analyzed with an algorithm using the particle swarm optimization (PSO) method. The powers from wind and solar energy were adopted as distributed power generation (DG) sources. The result of this study showed that the position of the access of the DG to the network is important as it affects the benefit and risk of the power supply for the network. We tested three different connections of the DG to the network, which had a 10% difference in the maximum power supply in the network. Along with the appropriate position of the DG access, the consideration of the risk assessment and the risk-taking also had a significant effect on the efficient management of the network. The model with the power supply risk function (R-PS) required a fourfold higher power supply from the DG, yielding a higher power supply (11%) and overall benefit (44%) than those without the risk function. The degree of risk-taking also affected the management of the network as the result revealed that power supply management with high risk-taking needed less power from the DG (14%), less power supply (2%), and had one-third less overall benefit than those with low risk-taking. We expect the method and results in this study to provide a model for the effective management of a power distribution network with power from DG sources.